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Mechanical Properties of Viscoelastic Materials
Jacob Feste
October 2, 2014

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Abstract
This experiment performed to determine the material properties of various viscoelastic
polymers, including low density polyethylene (LDPE), high density polyethylene (HDPE),
Neoprene, and Red Silicone. The material properties primarily measured in this experiment
were creep and stress relaxation, due to each material’s time-dependent identity as a
viscoelastic polymer, of which illustrates the time-dependent plastic deformation under
constant load as well as the time-dependent decrease in stress under a constant strain for
these materials. To obtain our results, three replicas each of LDPE and Neoprene were
measured and inserted into an Instron machine to undergo a hysteresis test. Two replicas each
of HDPE and Red Silicone were also measured and inserted into an Instron machine to undergo
a stress relaxation test. The data obtained by the Intron was then used in Microsoft Excel to be
converted into stress relaxation and hysteresis values of which were graphed on Excel. The
results concluded a much higher amount of average energy lost in LDPE (38,653 N/m2) in
comparison to Neoprene (3,261.5 N/m2) for the hysteresis test, and a much larger change in
stress over time, or relaxation modulus, for HDPE in comparison to Red Silicone for the stress
relaxation test.
Introduction
The purpose of this experiment is to determine the material properties of different
viscoelastic polymers. Viscoelastic polymers display a time-dependent deformation in result of
an applied stress. Viscoelasticpolymers also lose energy during loading and unloading. These
properties are what separate a viscoelastic polymer from an elastic polymer. For this
experiment, the viscoelastic material properties of four different viscoelastic polymers were
measured, specifically low density polyethylene (LDPE), high density polyethylene (HDPE),
Neoprene, and Red Silicone. These polymeric solids are viscoelastic in their rubbery states,
which includes all of these polymers at room temperature and therefore this experiment was
performed in such an environment. As far as the chemical properties of these materials go,
LDPE and HDPE have repeat units of C2H4, a simple chemical makeup with a light molecular
weight. However, these two polymers differ in the fact that HDPE has linear geometry giving it
higher density, while LDPE shows signs of branching and has a lower density. Neoprene has
repeat units with a more complex makeup, H2CCHCClCH2 [1], giving it a higher weight than
LDPE and HDPE with polar side group, and is an excellent adhesive in solution [1]. Silicone
rubbers have the general repeat unit of H2CSiCH3O + 2ROH where R is an organic side group [2],
giving it a high molecular weight and somewhat of a complex structure. The Si-O bonds have a
high bonding energy making them chemically stable and a strong, but brittle elastomer with a
high melting point [2]. The material properties observed in this experiment specifically include

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creep for LDPE and Neoprene, and stress relaxation for HDPE and Red Silicone, both of which
are commonly used to illustrate the viscoelastic behavior of polymers. Creep is the amount of
time-dependent plastic deformation under a constant load. On the molecular level of a
viscoelastic polymer with a constant load, creep is affected by the way the molecules in a
polymer respond to a constant force with time. Both LDPE and Neoprene will exhibit the steps
of creep, including an initial elastic-like strain, a slowly increasing strain with time, and a back-
stress upon loading with the reverse processes upon unloading. However, the manner in which
the molecules react will differ due to differing chemical makeups, and affect the creep by how
well they can handle the initial load as well as their ability to rearrange and form new bonds
under a constant load until their able to produce a recoil stress. The creep of these polymers
can be illustrated via hysteresis test, which records this phenomena graphically as the change in
stress and strain over the creep period. The overall importance of the creep and hysteresis test
is the ability to determine the amount of energy each material loses between loading, which is
determined by the area in between the loading and unloading curves in the hysteresis graphs.
This property is important in determining how well a viscoelastic polymer can handle loading
and unloading, which is important medically in areas that include constant load bearings such
as prosthetics. Between LDPE and Neoprene, it can be hypothesized that LDPE will lose a
greater amount of energy than Neoprene due to the simple and branched chemical makeup of
LDPE and the more complex, and tougher properties of Neoprene. The other two polymers,
HDPE and Red Silicone, used a stress relaxation test to determine their viscoelastic behavior.
Stress relaxation is a viscoelastic polymer’s decrease in stress under a constant strain with
respect to time. The stress relaxation test graphically illustrates the material’s stress in
response to time. Properties such as how well a polymer can redistribute the load to decrease
stress. Also, the relaxation modulus can be computed from it, which demonstrates the
magnitude at which stress is relieved over time numerically. It can by hypothesized that HDPE
will undergo a large initial change in stress with time as well as have a high relaxation modulus
due to the simplicity and linearity of its chemical structure as well as it’s ductile properties. Red
Silicone is a bulky, complex, and chemically stable elastomer, and can be hypothesized that its
strength will give a small initial change in stress with a small relaxation modulus. The
information above proves the hysteresis and stress relaxation tests ideal for demonstrating the
viscoelastic behaviors such as the creep and relaxation properties of the chosen viscoelastic
polymers.

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Procedures
To begin the experiment, two replicas each of HDPE and Red Silicone dogbone samples
as well as three replicas each of LDPE and Neoprene dogbone samples were obtained. All of
these replicas were labeled numerically. The HDPE and Red Silicone replicas were given a stress
relaxation test. To perform this, each replica had its width and thickness measured using
calipers. These values were recorded in a lab book. A replica was then gripped tightly into an
Instron machine and its length from the central position measured using calipers and recorded
in a lab book. The load cell and extension of the Instron were zero’d. The Instron machine was
then set to run a ten minute stress relaxation test. For the HDPE replicas, the machine was set
to a rate of 2.5 mm/min and hold extension of 1 mm. For the Red Silicone replicas, the machine
was set to a rate of 25 mm/min and a hold extension of 10 mm. The measured data from the
Instron was transferred to an excel file and manipulated into a stress relaxation graph. These
procedures were repeated for each replica of both the HDPE and Red Silicone samples. The
LDPE and Neoprene samples were given a hysteresis test by the Intron. To perform this, each
replica had its width and thickness measured using calipers and the values recorded in a lab
book. Each replica was then gripped tightly into the Instron machine, with each replica having
its length measured from the central position using calipers and recorded into a lab book. Then,
the Instron machine had its load cell and extension zero’d before use. The Instron machine was
set to run a hysteresis test for each sample with an extension and return rate of 50 mm/min
and reverse extension of 10 mm. The measured data from each replica by the Instron was then
transferred to an excel file and manipulated into a hysteresis graph. Finally, all of the data from
both tests was saved on a flash drive for later use.
Multiple steps were performed to translate the Instron data to graphically represent
hysteresis for LDPE and Neoprene and stress relaxation for HDPE and Red Silicone. The data
was first transferred to Microsoft Excel for manipulation. For the hysteresis data, the stress was
calculated and plotted against strain for each replica. The data for each replica of LDPE and
Neoprene were plotted together on the same graph for each respective sample type. For the
stress relaxation data, the stress was calculated and plotted against time for each replica. The
data for each replica of HDPE and Red Silicone were plotted together on the same graph for
each respective sample type. In addition, a representative replica for each sample type was
plotted together for comparison, which was done for both types from the hysteresis and stress
relaxation tests. Finally, for the hysteresis tests, the area between the loading and unloading
curves was calculated for each replica. This was then averaged and the standard deviation was
calculated as well.

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Results
Hysteresis of LDPE and Neoprene
Material Average Energy Lost (N/m2) Standard Deviation
LDPE 38653 1508.09
Neoprene 3261.5 644.85
Table 1: Average Energy Lost and Standard Deviation for LDPE and Neoprene
Figure 1: Hysteresis of LDPE
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Figure 2: Hysteresis of Neoprene
Figure 3: Hysteresis Comparison of LDPE and Neoprene
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Hysteresis Test: LDPEvs. NeopreneTest 1 Replicas
LDPE Test 1
Neoprene Test 1

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Stress Relaxation of HDPE and Red Silicone
Figure 4: Stress Relaxation of HDPE
Figure 5: Stress Relaxation of Red Silicone
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Figure 6: Stress Relaxation Comparison of HDPE and Red Silicone
Discussion
For the hysteresis tests, LDPE underwent much more stress with respect to strain than
Neoprene. In figure 1, LDPE displayed elastic hysteresis up to around a stress of 75,000 N/m2
and a strain of around 0.01 during loading. During loading it then showed signs of plastic
hysteresis up to a stress of almost 250,000 N/m2 before stabilizing a small amount until it
unloading began at a strain of around 0.16. During unloading, plastic hysteresis kept occurring
until a strain of around 0.12 and a stress of about 100,000 N/m2, of which it then began to
elastically deform until the stress returned to 0 N/m2, of which it returned to a strain of around
0.04. This data shows that LDPE underwent plastic hysteresis as it returned to a strain of about
0.04 with no stress applied. Neoprene in figure 2, on the other hand, underwent a much smaller
amount of plastic hysteresis and was plastically deformed much less after unloading at a strain
of around 0.015. During loading, it provided a seemingly linear slope with a present but small
amount of non-linearity which suggests a primarily elastic hysteresis. It maxed out in stress
right before unloading at around 25,000 N/m2 at the strain of around 0.16, a value much less
than LDPE. It followed a similar pattern while unloading as it did loading and returned to a
strain of around 0.015. This data proves it underwent a small amount of plastic deformation. As
far as how much energy was lost during hysteresis, LDPE lost an average amount of 38,653
N/m2 with a standard deviation of 1,508.09 while Neoprene lost an average amount of 3,261.5
N/m2 with a standard deviation of 644.85. Energy is lost during plastic hysteresis due to the
0 100 200 300 400 500 600 700
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Stress Relaxation: Red Silicone vs. HDPETest 1 replicas
Red Silicone Test 1
HDPE Test 1

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permanent breaking of bonds while plastically deforming, while no energy is lost during elastic
hysteresis as the bonds remain intact. As LDPE underwent a large amount of plastic hysteresis,
it lost a very large amount of energy, while Neoprene didn’t lose near as much energy which is
reflected by its small amount of plastic deformation. In conclusion, Neoprene proved to be
much tougher than LDPE due to its much smaller amount of energy lost during loading and
unloading while LDPE was more viscoelastic as it showed signs of relieving stress towards the
end of loading while Neoprene didn’t. Neoprene showed to be more elastic than viscous as it
underwent primarily elastic hysteresis and had an almost linear pattern reflecting elastic
deformation and plastically deformed very little. LDPE was much more viscous than elastic as it
underwent much more plastic deformation than elastic. These results reflect the chemical
structures of the polymers. LDPE’s low molecular weight and density, branching, and high bond
energy between the carbon atoms are displayed as it took a large amount of stress to break the
bonds and perform plastic deformation, but when those bonds were broken a lot of energy was
lost as a result. Neoprene’s complex, heavy, bulky structure lowered the stress much more than
LDPE as the load was spread over more molecules. Therefore much less bonds were
permanently broken from plastic deformation and less energy was lost. LDPE’s simple structure
also helps explain its ability to rearrange, unlike Neoprene. Experiments that involve the
intentional breaking of bonds to detect rearrangement or the transfer of energy under constant
load could be performed to support this conclusion. The other two viscoelastic polymers, HDPE
and Red Silicone, underwent stress relaxation tests. For HDPE in figure 4, the initial stress
quickly climbed to around 350,000 N/m2 at around 20 seconds. It quickly brought the stress
down to around 220,000 N/m2 after about 5 seconds then proceeded to gradually decrease to
around 150,000 N/m2 after 10 minutes, more than half the initial stress. For Red Silicone in
figure 5, the initial stress reach about 3,700 N/m2 at around 35 seconds, and quickly receded to
around 3,000 N/m2 10 seconds later. HDPE showed much more stress relaxation than Red
Silicone, as it was able to relieve almost 130,000 N/m2 in only 5 seconds and gradually declined
to more than half its initial stress after 10 minutes, which was much more than Red Silicone
could relieve. Stress relaxation is affected by a viscoelastic polymer’s ability to redistribute
force, therefore the greater the stress relaxation the more viscoelastic a polymer is. The
chemical structure of each of these polymers reflects these results. HDPE is linear and has a
very high density, therefore the close proximity of the molecules makes it easier for a load to be
distributed throughout. Red Silicone has a much more complex, bulkier structure with the
molecules more spread out than HDPE, resulting in less capability to distribute force. Possible
mechanisms that could explain this ability to “move” force include the ability of a polymer to
rearrange itself to have more molecules at the source. For instance, HDPE is linear and very
dense, allowing itself to rearrange well and pack in more molecules at the source for which the
load to be distributed to. The carbon bonds are also easier to stretch than the silicone-oxygen
bonds in Red Silicone due to lower bonding energy, which could allow the polymer to stretch
out and aid at the source. Red Silicone could use the same methods, just not as well as HDPE.

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Conclusion
The objective of this experiment was to determine the viscoelastic behaviors of various
polymers, specifically HDPE, LDPE, Neoprene, and Red Silicone. To illustrate these behaviors,
stress relaxation tests were performed on HDPE and Red Silicone while hysteresis tests were
performed on LDPE and Neoprene. Samples of each of these polymers were measured and
performed their respective tests via an Instron and the data was manipulated and used to
graphically represent the hysteresis of LDPE and Neoprene and the stress relaxation of HDPE
and Red Silicone. These graphs were interpreted and their results concluded. In conclusion, the
viscoelastic behaviors measured in this experiment support the various hypotheses derived
beforehand based on the molecular structure and properties for LDPE, HDPE, Neoprene, and
Red Silicone. For the hysteresis tests, it was hypothesized that LDPE would lose more energy
than Neoprene due to the chemical makeup of the two. This proved to be accurate as LDPE lost
more than ten times the amount of energy than Neoprene did: 38,653 N/m2 compared to
3,261.5 N/m2. It was also predicted that Neoprene would undergo much less stress due to its
bulkiness and wouldn’t plastically deform as much during loading and unloading due to its
greater stability. These also proved to be true as Neoprene underwent much less levels of
stress, maxing out around 25,000 N/m2 compared to 240,000 N/m2, and had much less plastic
deformation after unloading, 0.016 compared to 0.04. This was illustrated in Table 1 and Figure
3. For the stress relaxation tests, chemical properties were also used to hypothesize that HDPE
will undergo greater stress while having a higher relaxation modulus than Red Silicone. Figure 6
supports this prediction as HDPE was shown to decrease its stress levels much greater and
faster while having a tremendously high initial stress; dropping from 350,000 N/m2 to 150,000
N/m2 after 10 minutes. These results provide an excellent illustration of the viscoelasticity of
each material and are important in medical world in the determination of which material to use
for various applications with various environments. While these results may be a pretty
accurate resource of the measured properties, sources of error could have been present to
mildly throw off the values. For instance, if a measurement was off by just a small amount, the
stress and strain values would be thrown off as well for each data point. Also, the replicas
could’ve been not as tightly locked in during the Instron tests which could sway the values as
well. Bumping into the table or Instron during the test or even human errors such as mixing up
the replica numbers could’ve swayed results too.
References
[1] "CIEC Promoting Science at the University of York, York, UK." Buta-1,3-diene. N.p., n.d. Web. 13
Oct. 2014.
[2] Solutions, Creating Tomorrow’s. "Silicones: Compounds and Properties."Elastomers, Plastics &
Composites Silicones (n.d.): n. pag.Www.wacker.com. Wacker. Web.

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Effect of Polymer Composition on the
Compressive Properties of a Porous Scaffold
Jacob Feste
November 6th
, 2014

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Abstract
This experiment was performed to determine the effect of polymer composition on the
compressive properties of a porous engineering scaffold. The porous engineering scaffolds
consisted of Dichloromethane (DCM), NaCl, and PLGA. The technique of salt-leeching was used
to create these scaffolds. A total of two different scaffolds were created, one using low
molecular weight PLGA (7,000-17,000 g/mol) and the other using high molecular weight PLGA
(38,000-54,000 g/mol), in order to determine the effect that differing mass concentrations of
the polymer PLGA have on the compressive strength of a porous scaffold. After performing a
compressive test and manipulating the data into stress-strain graphs, it was determined that
the higher molecular weight PLGA had a greater compressive strength than the lower molecular
weight PLGA as predicted. The yield stress on average was around three times greater for the
higher molecular weight PLGA than the lower molecular weight. It was concluded that the
higher the molecular weight of a polymer in a porous scaffold, the greater the compressive
strength of that scaffold.
Introduction
This experiment utilizes a porous tissue engineering scaffold for bone regeneration
made from a degradable polymer in order to determine the effect that polymer composition,
specifically mass concentration, has on the compressive strength of the scaffold. A porous
scaffold includes a polymer in solution form and porogens, of which through processes such as
salt leaching form an overall porous structure [1]. To obtain a polymer in solution form,
methods such as dissolving the polymer in a polar solvent such as dichloromethane (DCM) may
be used. The porogens of a porous scaffold are substances that create pores, such as salt in salt-
leaching. Salt-leaching was the process used to obtain the porous scaffold in this experiment.
Salt-leaching is used to create pores in a structure. [1] It creates these pores by first pouring a

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material for the scaffold, a polymer in solution form in this case, over an array of porogens, or
the substance that creates the pores. Salt serves as the porogens for salt-leaching. The polymer
is now allowed to solidify by solvent evaporation. Finally the salt is dissolved by the addition of
deionized water, or is leached out in a solvent for the porogen [1]. A porous scaffold of the
polymer solution remains. Samples of these polymer scaffolds may be used and tested for
different properties, such as their compressive properties. The compressive strength is a
measure of the capacity of a material to withstand loads attempting to reduce its size [2]. The
yield strength is the maximum amount of stress a material can withstand before plastic
deformation [3]. Together these properties serve as excellent tools for compressive property
analysis.
For this particular experiment, two separate forms of a porous scaffold were created.
Each of them were created by salt-leaching using the same amounts dichloromethane (DCM)
and NaCl, but with differing molecular weights of PLGA (7,000-17,000 g/mol and 38,000-54,000
g/mol). The differing molecular weights were used in order to detect the impact that the
molecular weight of a polymer has on the compressive properties of a porous scaffold made
from the polymer. Based on the knowledge of higher mass concentrations giving higher yield
strengths, it was hypothesized that if the mass concentration of the polymer in a porous
scaffold increases, then the yield strength will increase as well. That is, molecular weight 2
(38,000-54,000 g/mol) will undergo a higher yield stress and strain than molecular weight 1
(7,000-17,000 g/mol).
Procedures
We began our experiment by weighing out the required amount of polymer PLGA in a
weight boat to obtain a molecular weight 1 value of 7,000-17,000 g/mol and a molecular weight
2 value of 38,000-54,000 g/mol. We transferred these two molecular weight values separately
to a 15ml centrifuge tube. Under a chemical hood, we added dichloromethane (DCM) using
glass pipets to each molecular weight group. We then tightened the lid and mixed the groups
separately until all of the polymer was dissolved for each group. Next, 180-425 um of NaCl was
weighed out and added into the lid of a 50ml centrifuge tube for each group separately and
such that the salt layer was evenly dispersed within the mold. Under the chemical hood, 6ml of
each group of polymer solution was poured into their different molds and gently mixed to
evenly disperse the salt. The two molds were left in the hood overnight. The next day, a biopsy
punch was used to punch out one sample of molecular weight 1 scaffold (due to error in
preparation) and three samples of molecular weight 2 scaffold. The punched scaffolds were

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individually put in 50ml tubes containing deionized water. The tubes were labeled by their
sample type and number and put into a shaker. The samples were then washed and dried.
One week later, we measured and recorded the dimensions of each sample before
performing a compressive test. Using the Bluehill software we created a new test with the
method provided (Lab3 Compressive). We then placed the samples at the center of the
compression anvil, individually for each one. Next we used the “jog” button to position the top
anvil right at the top for each sample scaffold. The load cell and extension of the instron were
zeroed for each sample. We began the test for each sample, making sure that the compression
stops before the anvils touched each other. All observation were recorded during the study.
Finally, data was obtained for each sample and the raw data for each samples saved on a flash
drive for analysis.
Results
Figure 1
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Molecular Weight 1 Stress vs Strain
Sample 1
Sample 2
Sample 3

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Figure 2
Yield Stress (Pa) Yield Strain
MW1 Sample 1 5431638.701 0.31657896
MW1 Sample 2 5431638.701 0.31657896
MW1 Sample 3 5431638.701 0.31657896
MW2 Sample 1 15002459.6 0.18444115
MW2 Sample 2 16075619.6 0.20219581
MW2 Sample 3 16482200 0.22890422
Table 1
Yield Stress (Pa) Yield Strain
Molecular Weight 1
mean 5431638.701 0.31657896
Std dev 0 0
Molecular Weight 2
mean 15853426.4 0.20518
Std dev 764483.7 0.02238
Table 2
Strain- t value=-4.310, p value=0.975
Stress- t value=11.806, p value= 0.00355
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Molecular Weight 2 Stress vs Strain
Sample 1
Sample 2
Sample 3

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Discussion
PLGA with molecular weight 2 (38,000-54,000 g/mol) had an average yield strength of
15,853,426.4 Pa, with a standard deviation of 764,483.7. This was much higher, almost three
times higher, than the molecular weight 1 (7,000-17,000 g/mol), which had an average yield
strength of 5,431,638.701 Pa. Molecular weight 1 had a higher average yield strain of
0.31657896 while Molecular weight 2 had an average yield strain of 0.20518 and a standard
deviation of 0.02238. Molecular weight 1’s higher average strain value is most likely due the
error in which all three of its samples clumped into one giant sample during preparation, and it
can be suggested that the true value of an individual sample would be much lower than that
and lower than the average yield strain of molecular weight 2. In terms of statistical analysis, a
one tailed T-Test was used to illustrate the extent at which molecular weight 2’s mean stress
and strain values were greater than molecular weight 1’s as hypothesized. The stress t-value
was 11.806 with a p value of 0.00355, which supports the hypothesis that molecular weight 2
has a higher compressive strength and rejects the null hypothesis that molecular weight of a
polymer in a porous scaffold has no effect on compressive strength. The strain t-value was -
4.310 with a p value of 0.975. The negative value was again the result of a three sample
clumped molecular weight 1 and it can be suggested that if prepared correctly the value would
be positive. With the p value being less than 2 this also rejects the null hypothesis, but in the
wrong direction as indicated by the negative t value (due to error). Overall, the data serves as
an acceptable representation that a higher molecular weight polymer in a porous scaffold will
have a higher average yield stress and, if performed correctly, would also hold a higher yield
strain. The yield stress data proves to be pretty accurate in the fact that molecular weight 2 has
a higher average. This is due to yield stress being a function of force and cross sectional area at
the time of deformation; while molecular weight 2 may have around a third of the cross
sectional area than molecular weight 1 (three separate samples as opposed to one giant
sample), the force is spread around about three times as much area for molecular weight 1 and
therefore the force at yielding is around three times what it would be if it were three separate
samples. This essentially implies the average yield stress being similar to what it would be for
three separate samples of molecular weight 1. Yield strain, on the other hand, is greatly put off
by the error of the clumping of molecular weight 1. As yield strain is a function of change in
length (elongation) and initial length, it is much more important for the samples of the different
molecular weights to be similar in initial length. With the molecular weight 1 sample having a
much greater width than the average width of the molecular weight 2 samples (around three
times as much), it has a much greater resistance to strain yielding than it should if it were
broken into three samples as it has much more support.

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The reason for our experimental setup, the use of a chemically identical polymer with
two different molecular weights in a porous engineering scaffold, was to determine the effect
that polymer composition (molecular weight) has on the compressive properties of a porous
tissue engineering scaffold. By holding the other properties of the scaffold constant, (same
amount of DCM, 20% porosity, same amount of NaCl) for each of the two molecular weight
polymers, we were able to determine how the molecular weight of a polymer in a porous
scaffold effects the scaffold’s yield stress and strain in a compression test. Sources of error
includes that listed in the previous paragraph, the clumping of molecular weight 1 into one
giant sample, as well as not zeroing the instron before performing the compression tests. While
the data was manipulated to assess this problem, error could still be present due to this.
Human error could also lie in areas such as not stopping the test at the right time or inaccurate
measurements, which could also sway the results slightly. Overall our experiment confirmed
our hypothesis that a higher molecular weight polymer in a porous scaffold would have a higher
yield stress and strain. The measured yield strain data doesn’t support this due to error but as
described in the previous paragraph can be assumed to support the hypothesis had the samples
been separated as intended. Based on our results, molecular weight 2 would be more suitable
for a bone-regenerative scaffold as it a stronger material than molecular weight 1, which is a
favorable property for bone. Another property to consider in bone regeneration scaffolds is
biodegradability, of which PLGA is very biodegradable. As far as other factors go that aren’t fully
illustrated by PLGA are factors that are more suitable with ceramics. Ceramics prove to be
excellent bone regeneration scaffolds as they are generally tougher and less ductile than
polymers; bone regeneration scaffolds need a strong, solid hold on the bone while not
plastically deforming much at all so the bone can regenerate where it needs to. Future
directions of this experiment could include areas that display similar compressive properties
but with a different variable of polymer composition, such as the use of HDPE and LDPE to
determine the impact that density has on the compressive properties of a porous scaffold with
these polymers. On the other hand, they could include the impact that chemically identical
polymers with differing molecular weights have on other mechanical properties of porous
tissue engineering scaffolds, such as stress relaxation or toughness. Limitations of this
experiment include the large degree of time it takes to prepare a scaffold, which forces errors
like the clumping together of samples to be acceptable under a small time window. Another
limitations may lie in the instruments used such that they may give small errors in
measurement, or that the compression test much be stopped before the anvils touch as to not
skew the data, etc. These limitations may be addressed by having access to more materials in
order to make extra samples in case of error, or using instruments better suited to perform
compression tests on polymers with less reliability on the user (less room for human error).

18. 18 | P a g e
Conclusion
The objective of this experiment was to determine the effect that polymer composition,
specifically molecular weight, has on the compressive properties of a porous tissue engineering
scaffold. To obtain this objective, two groups of porous scaffolds were created by salt-leaching
using identical amounts of salt (NaCl), dichloromethane (DCM), structurally identical polymers
(PLGA), and identical porosity (20%); but differing only in the molecular weight of the polymer
(PLGA) used (7,000-17,000 g/mol and 38,000-54,000 g/mol). Samples from each of the two
groups were given a compression test by an instron in order to quantitatively measure their
compressive properties. The results illustrated by table 2 indicate the higher molecular weight
PLGA had a much greater average yield stress than the lower molecular weight PLGA:
15,853,426.4 Pa as opposed to 5,431,638.701 Pa. The one tailed t-test performed on the stress
values gave a t value of 11.806 and a p value of 0.00355, indicating that the null hypothesis that
the molecular weight of the polymer used in a porous scaffold has no effect on yield stress was
rejected, and that the stress values for a polymer with a higher molecular weight were much
greater. The results illustrated by table 2 also indicate that the lower molecular weight PLGA
had a higher average yield strain than the higher molecular weight PLGA, 0.31657896 as
opposed to 0.20518. The one tailed t-test performed on the strain values gave a t value of -
4.310 with a p value of 0.975, also indicating a rejection of the null hypothesis but in the
direction in which the lower molecular weight PLGA has higher yield strain values. As discussed
previously in detail, error in the clumping of the lower molecular weight samples into one giant
sample was most likely the cause of this outcome. By reasoning and reflecting on the average
yield stress results it can be determined that the higher molecular weight PLGA would have
higher yield strain values than the lower molecular weight PLGA would if the samples hadn’t
been clumped together.
Our hypothesis was that the higher molecular weight PLGA would have both a greater
average yield stress and average yield strain, which correlates to a greater compressive
strength. It is concluded by the experiment that our hypothesis was, for the most part,
supported; the higher molecular weight PLGA did indeed have a higher average yield stress and
is suggested to have a higher average yield strain had the clumping of the lower molecular
weight samples not occurred. Although the experiment would have to be repeated correctly to
completely solidify the higher average yield strain implication for the higher molecular weight
polymer, this experiment can generally conclude that the higher molecular weight of a polymer
used in a porous tissue engineering scaffold, the higher the yield stress and yield strain of the
overall scaffold. On a broader scale, the compressive properties of a porous scaffold are indeed
effected by the molecular weight of the polymer used. While the experiment may give these
general outcomes, there were many sources of error that could sway these results. The primary

19. 19 | P a g e
error in this experiment was the clumping of the molecular weight 1 samples into one giant
sample, which had a large impact on the results regarding the strain values and could’ve also
had an impact on the stress values as well. Reflecting on the mechanism of yield strain and
referring to the yield stress values helped to assess this error. Perhaps the next greatest source
of error was the instron not being zeroed before the compression tests were performed. This
error was also assessed by manipulating the data to what it would be if the instron had been
zeroed. Finally, human errors such as stopping the compression test at the correct time and
measurement errors could’ve also had an impact on the data.
References
[1] "UWEB :: Research : Biomaterials Tutorial." UWEB :: Research : Biomaterials
Tutorial. UWEB, n.d. Web. 18 Nov. 2014.
[2] "Glossary of Materials Testing." Compressive Strength. N.p., n.d. Web. 18 Nov.
2014.
[3] "Yield Strength - Strength ( Mechanics ) of Materials - Engineers Edge." Yield
Strength - Strength ( Mechanics ) of Materials - Engineers Edge. N.p., n.d. Web.

20. 20 | P a g e
The Impact of Scaling Effects on Nano Composite MEMS Devices: Mechanical and Electrical
Properties
Jacob Feste
University of Arkansas, Biomedical Engineering, jtfeste@email.uark.edu

21. 21 | P a g e
Abstract
The goal of this experiment was to determine the mechanical and electrical properties
of a PDMS nanoparticle composite with various percentages of carbon black. These properties
are also measured in order to relate them to characteristics associated with a composite
material, such as the Rule of Mixtures (ROM). Mechanical properties were measured via tensile
testing with carbon black percentages ranging from 0%-20% (in multiples of two), with brass,
polypropylene, and steel measured for reference. The results indicated a weak material and a
linear increase in tensile strength as carbon black percentage increased, satisfying the ROM for
this composite. Electrical properties were determined by measuring output voltages relating to
changes in resistance in order to determine changes in resistivity. A Wheatstone bridge circuit
staged by an instrumentation amplifier and measured through an oscilloscope was used with
carbon black percentages ranging from 14%-19%. The results included a large degree of error
for the 14%-16% samples, however the results from the remaining samples provided enough
accuracy to support an increase in resistivity as carbon black percentage decreases.
Nomenclature
R= Resistance (Ohms)
L= Length (m)
A= Cross-sectional Area (m2)
ρ= Resistivity (Ohm*m)
V= Voltage (V)
I= Current (A)
σ= Stress (Pa)
F= Force (N)
ԑ= Strain
E= Elastic Modulus (Pa)
Introduction

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A composite material is defined as a single material composed of a mixture of two or
more materials. Composite materials have a wide range of application due to the dependence
of their properties on the individual properties of the various materials they are composed of.
This relationship allows for the engineering of a composite material with desired properties
based on the combined properties included in its composition. However, for composite
materials, properties such as mechanical properties may be negatively influenced by the
addition of one material to the surface of another. When two materials of different shapes and
sizes are combined, a small region of space is created where their dimensions are not perfectly
identical, resulting in a less stable and less uniform structure. The “porous” identity, for
example, interrupts conduction paths, decreases mechanical strength, and other properties
that favor a single, uniform structure. It is therefore desired to minimize this aspect in order for
a composite material to maximize its desired, combined properties and minimize the negative
impacts inherently involved with the combination of different materials. Minimizing the degree
of porosity in composites is performed by increasing the impact that an added material’s
surface (area) has compared to the overall volume. The impact that a material’s surface has on
its total volume increases as the size of a material decreases due to the effect of scaling laws.
These laws support an increased area-to-volume ratio with a decrease in length due to the
exponential relationship between length, area, and volume [1]. Therefore, the addition of
particles of smaller sizes are desired for the production of effective composite materials.
Nanoparticles fulfill this requirement, making nanoparticle composites effective at retaining the
combined material properties.
The nanoparticle composites involved in this experiment are aimed to highlight the
properties of a material with an elastomeric matrix phase (PDMS) and carbon black. The
composites are formed using DDPOST, a process that allows the formation of a thick layer of
polymer composite using micro- and nano- particles with polymer matrix [1]. The resulting
polymer composite can be made electrically, mechanically, or chemically active by selecting
specific particle and matrix materials [1]. The nanoparticle composites of this experiment were
formed to allow chemical activation in order to serve as Micro-Electro-Mechanical Systems
(MEMS) based corrosion sensors. The carbon black nanoparticle inclusions are considered
electrically conductive nanoparticles and are applied to the PDMS matrix. When swelling and
etching agents are applied to this mixture, the PDMS matrix swells to a certain degree
dependent on the concentration of the swelling agents or chemical vapor exposure. Upon
swelling, the PDMS volume expands and extends the electrical pathways of the conductive
carbon black suspension [2]. The resistivity changes are then measured upon swelling
equilibrium in order to determine vapor concentration. Resistivity is given by the following
relationship:
Equation (1): 𝑅 = 𝜌(
𝐿
𝐴
) or 𝜌 = 𝑅(
𝐴
𝐿
)
PDMS has a high resistivity around 1*1013-1*1015 ohm*m [3]. When swelling occurs, the
resistivity value increases due to the extended electrical pathways [2]. These changes may be
evaluated to determine concentration due to the percolation theory. This theory claims that
clusters of particles attached to the surface of a material, such as the clusters of carbon black
nanoparticles attached to the PDMS matrix, accurately represent a uniform structure until the

23. 23 | P a g e
randomness and separation of these clusters reaches a percolation limit at a certain percentage
of clustered particles [4]. At this limit, there is no longer a possible path connecting each
cluster, where this probability increases exponential as the percentage of clusters decreases.
The small sizes of the nanoparticles give an advantage in this aspect. Therefore, the
concentrations of the swelling and etching agents may be represented by the concentration of
removed nanoparticles until the percolation limit is reached and are given by the changes in
measured and known resistivity. Chemical vapor will provide effects similar to those of the
swelling and etching agents, allowing its concentration to be evaluated. Changes in resistance
must be measured in order to measure the changes in resistivity to make concentration
assumptions. This process is done with the use of a complex circuit and resistance changes
measured by an LCR meter. The relationship between voltage, current, and resistance is given
by:
Equation (2): 𝑅 =
𝑉
𝐼
or 𝑉 = 𝐼𝑅
The derivation of this relationship:
Equation (3): 𝑑𝑉 = 𝑅𝑑𝐼 + 𝐼𝑑𝑅
Is utilized in order to form a circuit with variable resistance only. By maintaining a constant
current, this equation becomes further reduced to:
Equation (4): 𝑑𝑉 = 𝐼𝑑𝑅
Therefore, a changing input voltage and constant current are necessary for the circuit to
measure changes in resistance. However, the voltage values must remain constant in order to
measure these changes. A “constant” voltage may be manipulated from a variable voltage
source by using voltage dividers and op amps. The circuit begins with the use of a voltage
divider in order to output a reference voltage close to 1 volt for the rest of the circuit. The
voltage divider equation is as:
Equation (5): 𝑉𝑜𝑢𝑡 = 𝑉𝑖𝑛
𝑅1
𝑅1+𝑅2
By using an R1 (10 kΩ) resistor with resistance significantly higher than that of R2 (10 Ω), the
output voltage will always remain about 1 volt (0.999V). This circuit is followed by an op amp
voltage follower in order to maintain the constant voltage, followed by a 10 kΩ resistor in order
to return the output voltage to its true value by countering the effects of the much higher R1
resistor of the voltage divider and maintain constant current. This component is then followed
by another op amp with one input side grounded and the other connected to the LCR device for
measurements. The grounded component serves to form as an adequate zero voltage
reference level while also having the ability to absorb as much current as possible without
disturbing the voltage potential, ultimately maintaining the desired constant values of the
circuit. Our device uses a 4-point probe that measures the voltage difference between the
middle two probes. The outer two probes connect to the non-grounded side of the op amp and
the op amp’s output, sending the constant current through the two ends. The middle two
probes are separated by a specimen acting as a resistor, where these voltage values are

24. 24 | P a g e
measured following the addition of another voltage follower for the two probes to maintain
constant voltage for each path. The change in voltage across these two probes are measured to
determine the change in resistance, made possible by the constant current and voltage values
that are maintained throughout the circuit and differ only between these two points. The final
circuit is illustrated by figure (1) below.
In addition to the electrical properties of a nanoparticle composite, the mechanical
properties of these composites are also important. As stated previously, the combined
mechanical properties may be negatively influenced by the formation of such composites due
to inherent size differences between the components. Also, nanoparticles often reside in
clusters and are difficult to attach uniformly, giving rise to inherent randomness. The resulting
composites are often anisotropic in nature and therefore require estimates to determine
properties such as their overall mechanical strength. The Rule of Mixtures (ROM) may be
analyzed to provide these estimates. The ROM states that stress values are directly
proportional to the ratio of the volume of nanoparticles to that of the total composite [5].
Nanoparticles have an advantage in that they have more of a surface area effect in a composite
and therefore the remainder of the composite retains most of its volume and therefore
individual stress values. However, this property may eliminate the possible use of the ROM in
order to estimate the stress values for these composites types. The stress values may be
measured using a tensile test, where the samples are elongated until failure or until a certain
length, with the stress and strain values measured along the way. Stress and strain values are
given by the following relationships:
Equation (6): 𝜎 = 𝐸ԑ
Equation (7): 𝜎 =
𝐹
𝐴

25. 25 | P a g e
Equation (8): ԑ =
∆𝑙
𝐿
By comparing this data to the relationship given by the ROM, it is made possible to determine
whether or not the ROMremains accurate for the composite. Proportionality should be seen
between the stress values and different volume percentages. If this pattern is not given, it can
be suggested that the ROM does not apply. This experiment will measure the mechanical
properties of nanoparticle composites composed of PDMS and different volumes of carbon
black. By comparing the data for the different percentages, it is possible to determine their
proportionality and whether or not the composite follows the ROM.
Procedures
Materials
1. Carbon nanoparticles (Alfa Aesar, 45527[42nm, 100%], 39724 [42nm, 50%], H30253)
2. Sylgard® 184 silicone elastomer kit, Dow Corning (Midland, MI)
3. PASCO Scientific Plastic (AP-8222) and Metal (AP-8223) tensile test specimens
4. Mixing cups and stirrer
5. Isopropanol alchohol and De-ionized water
6. Metal Spatula
7. Glass Beakers (10mL)
8. Microscope glass slide (1”x3”)
9. Gold/Silver nanoparticles from synthesis lab (D. Chen)
10. Microfabricated electrodes with SU-8 molds
11. Tensile test specimen mold
12. Instrumentation amplifier with Wheatstone bridge circuit
13. Chemical vapor mixing and injection system
Equipment
1. Dell Inspiron 1764 Laptop
2. Tenma 72-9365 200Mhz Oscilloscope
3. BK Precision Power supply-Model9310
4. Agilent Technologies U1733P LCR Meter
5. PASCO Scientific Stress-Strain Apparatus (Tensile Tester)
6. Extech EX540 Multimeter/Thermocouple Reader (with Type-K thermocouple probe)
7. Digital Hot-plate
8. Digital Scale
9. Microsoft LifeCamStudio Webcam
Software
1. LabView
2. Instrument drivers/software

26. 26 | P a g e
Procedures
Electrical:
Preparation:
1. Weigh each particle of carbon black for desired volume ratios of 14%, 15%,
16%, 17%, 18%, and 19% when mixed with PDMS.
2. Convert the mass ratios from a systemic set of PDMS to the desired volume
ratios given in step 1.
3. Manually mix the particles and PDMS at 5 minutes each in a disposable
plastic mixing cup.
4. Squeegee the material into the pre-fabricated micro electrode SU8 mold by
using a standard microscope glass slide.
5. Release the mold after it has been cured for a couple of days.
LCR Measurements:
1. Connect either the multi-meter (Extech EX540), LCR meter (Agilent U1733P),
or Wheatstone bridge circuit staged by an instrumentation amplifier and
measured through an oscilloscope to the laptop and open their associated
link software.
2. Use the software to record all data digitally.
3. Begin with the multi-meter and measure the resistance and capacitance of
each sensor device.
4. Repeat step 3 with the LCR meter.
5. Connect the sensors to the bridge circuit, power up the circuit with the
power supply, and feed the output to the oscilloscope.
6. Repeat all the measurements with the oscilloscope while on a hotplate with
temperatures of (RT+10oC increments up to 100oC).
7. Monitor the temperature with the type-K thermocouple connected through
the multi-meter.
Note: Only the Wheatstone bridge method was performed for measurements.
Swelling and Resistance Measurements:
1. For each of the samples in preparation step 1, place a small disk of the
material into a 50mL beaker filled to the 40mL mark with one of the swelling
agents.
2. Use a 50:50 ratio of swelling and etching agents (12mL to 12mL) toluene and
acetic acid.

27. 27 | P a g e
3. Report the swelling process using a webcam (Microsoft LifeCamCinema
720p) that has been calibrated for its pixel resolution and controlled by the
LabView™ Vision® software.
4. Record the resistance when the swelling reaches equilibrium.
Note: This procedure was demonstrated but not performed for this
experiment.
Mechanical:
Preparation:
1. Mix PDMS with various carbon black densities (42nm; 50%, 100%).
2. Pour each sample into the prefabricated aluminum molds for natural curing
over one week.
3. Weigh each particle of Carbon Black for desired volume ratios of 0%, 2%, 4%,
6%, 8%, 10%, 12%, 14%, 16%, 18%, and 20% when mixed with PDMS.
4. Convert the mass ratios from a systemic set of PDMS to the desired volume
ratios given in step 1.
5. Manually mix the particles and PDMS at 5 minutes each in a disposable
plastic mixing cup.
Tensile Test Measurements:
1. Connect the PASCO Scientific Stress-Strain Apparatus’s Passport Rotary and
Force Sensors to the individual USB Link, then connect it to the laptop.
2. Start the DataStudio software and select the stress-strain apparatus
experiment.
3. Load the PASCO tensile test specimens and crank the rotary handle at a
steady rate until the specimen breaks.
4. Save the data and repeat for each sample.
5. Remove the nanoparticle PDMS composite specimen from its mold by
unscrewing the mold covers and load it into the Stress-Strain Apparatus.
6. Pull the specimen until breakage.
Results

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Figure (2): LCR Measurements using the Wheatstone bridge circuit measurement method for
PDMS composites with %17-19 Carbon Black.
Figure (3): Tensile test results for PDMS composites with % Carbon Black.
LCR Measurements for PDMS/Carbon Black (%) Composites Vin=0.9999 V Vsource=12 V
PDMS Composites with % Carbon Black 17% 18% 19%
Vout (V) Vout (V) Vout (V)
Sample 1 11.6 6.9 5.1
Sample 2 8.2 6.5 4.1
Sample 3 7.3 7.4 5.9
Sample 4 7.3 5.8 4.8
Sample 5 10.5 4.9 5.7
Average 8.98 6.3 5.12

29. 29 | P a g e
Figure (4): Tensile test results for total PDMS composite averages and different materials to
serve as references.
Discussion
The results of this experiment include results for LCR and tensile test measurements.
The swelling and resistance procedure was demonstrated but not performed. The LCR
measurements were taken using a Wheatstone bridge circuit staged by an instrumentation
amplifier and measured through an oscilloscope. The results are given by figure 2. According to
equation 2 and equation 4, these results suggest an average increase in resistivity for a PDMS
and carbon black composite as the percentage of carbon black decreases. This relationship was
expected and likely due to the more resistant, or less conductive, nature of PDMS compared to
carbon black. These measurements were intended to be taken for samples of 14%-19% carbon
black. While some successful results remained, measurements taken for the composites with
carbon black percentages less than 17% included a significant degree of error and therefore
inconclusive results. This error was likely due to the large possibility of error associated with
such a complex measurement system. It could also be due to errors involved in the preparation
process in which inaccurate volume percentages were produced. The results of the tensile tests
were much more accurate and are illustrated in figure 3 and figure 4. According to figure 3,
there was a general increase in ultimate tensile strength, or maximum stress before rupture, as
carbon black percentage increased. The stress values upon rupture were between 40,000-
60,000 Pascals for the samples with no carbon black while they were between 110,000-140,000
Pascals for those with 20% carbon black. The 10% samples had stress values between 80,000-

30. 30 | P a g e
100,000 Pascals before rupture, suggesting a linear relationship between carbon black
percentage and ultimate tensile stress. Most of the samples ruptured between stress values of
4-5. Many samples did not break, however, making it impossible to make correlations between
the carbon black percentages and rupture point strain values. Alone, PDMS has a relatively low
ultimate tensile strength around 15,000-90,000 Pascals. When a fraction of its volume is
replaced by carbon black, the combined properties should increase this value to satisfy the rule
of mixtures. The tensile test results exhibit a somewhat linear increase in stress values as
carbon black percentage increases, suggesting a valid ROMrelationship. Figure 4 illustrates the
average of the tensile test values combining each sample of each percentage, and compares
them to materials such as steel, brass, and polypropylene. Steel had the highest tensile strength
but was much more brittle, or broke with less strain, than the other materials. Brass also had a
high tensile strength but mimicked the ductility of the polymer materials, polypropylene and
the PDMS composite. Overall, the composite material was much weaker than the other
materials, likely due to its composition and identity as a nanoparticle composite. However, the
negative mechanical impacts associated with a composite material were minimized due to the
results suggesting ROMapplicability. Error was also a possibility for the tensile test
measurements as well due to testing being done manually. However, the measurements were
accurate enough to generate conclusive assumptions and disregard this error.
References
[1] Huang, Adam. "Experiencing Scaling Effects via Nano Composite MEMS Devices." University
of Arkansas, n.d. Web. 1 Nov. 2015.
[2] Huang, Adam, Victor Tak Sing Wong, and Chih-Ming Ho. "Silicone Polymer Chemical Vapor
Sensors Fabricated by Direct Polymer Patterning on Substrate Technique (DPPOST)." Sensors
and Actuators B: Chemical116.1-2 (2006): 2-10. Web.
[3] "Polydimethylsiloxane (PDMS)." CiDRA Precision Services. N.p., n.d. Web. 01 Nov. 2015.
[4] Gastner, Michael T. "Percolation Theory." Michael Gastner: Percolation Theory. Imperial
College London, n.d. Web. 01 Nov. 2015.
[5] Kopeliovich, Dmitri. "Estimations of Composite Materials Properties."Substech. N.p., 2 June
2012. Web. 1 Nov. 2015.